Experimental Construction of Very Large Scale DNA Databases with Associative Search Capability

Combinatorial PCR Method for Efficient, Selective Oligo Retrieval from Complex Oligo Pools

With the rapidly decreasing cost of array-based oligo synthesis, large-scale oligo pools offer significant benefits for advanced applications including gene synthesis, CRISPR-based gene editing, and DNA data storage. The selective retrieval of specific oligos from these complex pools traditionally uses polymerase chain reaction (PCR). Designing a large number of primers to use in PCR presents a serious challenge, particularly for DNA data storage, where the size of an oligo pool is orders of magnitude larger than other applications. Although a nested primer address system was recently developed to increase the number of accessible files for DNA storage, it requires more complicated lab protocols and more expensive reagents to achieve high specificity, as well as more DNA address space. Here, we present a new combinatorial PCR method that has none of those drawbacks and outperforms in retrieval specificity. In experiments, we accessed three files that each comprised 1% of a DNA prototype database that contained 81 different files and enriched them to over 99.9% using our combinatorial primer method. Our method provides a viable path for scaling up DNA data storage systems and has broader utility whenever one must access a specific target oligo and can design their own primer regions.

Random access DNA memory using Boolean search in an archival file storage system

DNA is an ultrahigh-density storage medium that could meet exponentially growing worldwide demand for archival data storage if DNA synthesis costs declined sufficiently and if random access of files within exabyte-to-yottabyte-scale DNA data pools were feasible. Here, we demonstrate a path to overcome the second barrier by encapsulating data-encoding DNA file sequences within impervious silica capsules that are surface labelled with single-stranded DNA barcodes. Barcodes are chosen to represent file metadata, enabling selection of sets of files with Boolean logic directly, without use of amplification. We demonstrate random access of image files from a prototypical 2-kilobyte image database using fluorescence sorting with selection sensitivity of one in 10⁶ files, which thereby enables one in 10^6N selection capability using N optical channels. Our strategy thereby offers a scalable concept for random access of archival files in large-scale molecular datasets.

Solving the maximal matching problem with DNA molecules in Adleman–Lipton model

The maximal matching problem (MMP) is to find maximal edge subsets in a given undirected graph, that no pair of edges are adjacent in the subsets. It is a vitally important NP-complete problem in graph theory and applied mathematics, having numerous real life applications in optimal combination and linear programming fields. It can be difficultly solved by the electronic computer in exponential level time. Meanwhile in previous studies deoxyribonucleic acid (DNA) molecular operations usually were used to solve NP-complete continuous path search problems, e.g. HPP, traveling salesman problem, rarely for NP-hard problems with discrete vertices or edges solutions, such as the minimum vertex cover problem, graph coloring problem and so on. In this paper, we present a DNA algorithm for solving the MMP with DNA molecular operations. For an undirected graph with [Formula: see text] vertices and [Formula: see text] edges, we reasonably design fixed length DNA strands representing vertices and edges of the graph, take appropriate steps and get the solutions of the MMP in proper length range using [Formula: see text] time. We extend the application of DNA molecular operations and simultaneously simplify the complexity of the computation.

Experimentally Constructing Semantic Models Based on DNA Computing

We propose a new DNA-based semantic model, constructed of DNA molecules, called asemantic model based on molecular computing(SMC). It is structured as a graph formed by the set of all (attribute, attribute value) pairs contained in the set of represented objects, plus a tag node for each object. Each path in the network, from an initial object-representing tag node to the terminal node, represents the object named on the tag. Inputting a set of input strands the forms object-representing dsDNAs via parallel self-assembly from encoded ssDNAs representing both attributes and attribute values (nodes), as directed by ssDNA splitting strands representing relations (edges) in the network. The success of experiments in constructing a small test model demonstrates that our proposed model suitably represents knowledge to storing vast amounts of information at high density.